This book covers game theory, economics of Information, and behavioral economics, using examples that cover such topics as the analysis of demand, cost, and market efficiency; the design of pricing strategies; and public policy analysis.

    Smoking has gone from doctor recommended to deadly. We used to think the Earth was the center of the universe and that Pluto was a planet. For decades, we were convinced that the brontosaurus was a real dinosaur. In short, what we know about the world is constantly changing.   But it turns out there’s an order to the state of knowledge, an explanation for how we know what we know. Samuel Arbesman is an expert in the field of scientometrics—literally the science of science. Knowl­edge in most fields evolves systematically and predict­ably, and this evolution unfolds in a fascinating way that can have a powerful impact on our lives.   Doctors with a rough idea of when their knowl­edge is likely to expire can be better equipped to keep up with the latest research. Companies and govern­ments that understand how long new discoveries take to develop can improve decisions about allocating resources. And by tracing how and when language changes, each of us can better bridge gen­erational gaps in slang and dialect.   Just as we know that a chunk of uranium can break down in a measurable amount of time—a radioactive half-life—so too any given field’s change in knowledge can be measured concretely. We can know when facts in aggregate are obsolete, the rate at which new facts are created, and even how facts spread.   Arbesman takes us through a wide variety of fields, including those that change quickly, over the course of a few years, or over the span of centuries. He shows that much of what we know consists of “mesofacts”—facts that change at a middle timescale, often over a single human lifetime. Throughout, he of­fers intriguing examples about the face of knowledge: what English majors can learn from a statistical analysis of The Canterbury Tales, why it’s so hard to measure a mountain, and why so many parents still tell kids to eat their spinach because it’s rich in iron.   The Half-life of Facts is a riveting journey into the counterintuitive fabric of knowledge. It can help us find new ways to measure the world while accepting the limits of how much we can know with certainty.

    Sandel takes up one of the biggest ethical questions of our time: Isn't there something wrong with a world in which everything is for sale? If so, how can we prevent market values from reaching into spheres of life where they don't belong? What are the moral limits of markets?In recent decades, market values have crowded out nonmarket norms in almost every aspect of life. Without quite realizing it, Sandel argues, we have drifted from having a market economy to being a market society.In Justice, an international bestseller, Sandel showed himself to be a master at illuminating, with clarity and verve, the hard moral questions we confront in our everyday lives. Now, in What Money Can't Buy, he provokes a debate that's been missing in our market-driven age: What is the proper role of markets in a democratic society, and how can we protect the moral and civic goods that markets do not honor and money cannot buy?

    Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.In AI Superpowers, Kai-Fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not.  But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.

    Focused on groups, rings and fields, this text gives students a firm foundation for more specialized work by emphasizing an understanding of the nature of algebraic structures. KEY TOPICS: Sets and Relations; GROUPS AND SUBGROUPS; Introduction and Examples; Binary Operations; Isomorphic Binary Structures; Groups; Subgroups; Cyclic Groups; Generators and Cayley Digraphs; PERMUTATIONS, COSETS, AND DIRECT PRODUCTS; Groups of Permutations; Orbits, Cycles, and the Alternating Groups; Cosets and the Theorem of Lagrange; Direct Products and Finitely Generated Abelian Groups; Plane Isometries; HOMOMORPHISMS AND FACTOR GROUPS; Homomorphisms; Factor Groups; Factor-Group Computations and Simple Groups; Group Action on a Set; Applications of G-Sets to Counting; RINGS AND FIELDS; Rings and Fields; Integral Domains; Fermat's and Euler's Theorems; The Field of Quotients of an Integral Domain; Rings of Polynomials; Factorization of Polynomials over a Field; Noncommutative Examples; Ordered Rings and Fields; IDEALS AND FACTOR RINGS; Homomorphisms and Factor Rings; Prime and Maximal Ideas; Gr�bner Bases for Ideals; EXTENSION FIELDS; Introduction to Extension Fields; Vector Spaces; Algebraic Extensions; Geometric Constructions; Finite Fields; ADVANCED GROUP THEORY; Isomorphism Theorems; Series of Groups; Sylow Theorems; Applications of the Sylow Theory; Free Abelian Groups; Free Groups; Group Presentations; GROUPS IN TOPOLOGY; Simplicial Complexes and Homology Groups; Computations of Homology Groups; More Homology Computations and Applications; Homological Algebra; Factorization; Unique Factorization Domains; Euclidean Domains; Gaussian Integers and Multiplicative Norms; AUTOMORPHISMS AND GALOIS THEORY; Automorphisms of Fields; The Isomorphism Extension Theorem; Splitting Fields; Separable Extensions; Totally Inseparable Extensions; Galois Theory; Illustrations of Galois Theory; Cyclotomic Extensions; Insolvability of the Quintic; Matrix Algebra MARKET: For all readers interested in abstract algebra.

    Maintaining the quality of presentation and student-focused pedagogy that have characterized previous editions, Understanding Human Communication, Ninth Edition, incorporates updated examples and coverage of current communication theory. It continues to equip students with effective communication skills that will make a difference in their everyday lives.New to the Ninth Edition: * New material on mediated communication, personal listening styles, deceptive communication, and informative speaking * A revised section on the Cumulative Effects Theory and more applications of communication in the workplace, within the family, and at school * Updated research and examples on negative/positive language and gender influences on communication * Improved design and pedagogy: case studies at the opening of each part, highlights at the beginning of each chapter, and completely annotated full speech outlines with accompanying sample speeches * Additional teaching and learning resources: Student Success Manual, Student Resources Disc, expanded Instructor's Manual and Test Bank, Instructor's Disc, and an extensive web site

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    In his most important work, The General Theory of Employment, Interest, and Money (1936), Keynes critiqued the laissez-faire policies of his day, particularly the proposition that a normally functioning market economy would bring full employment. Keynes's forward-looking work transformed economics from merely a descriptive and analytic discipline into one that is policy oriented. For Keynes, enlightened government intervention in a nation's economic life was essential to curbing what he saw as the inherent inequalities and instabilities of unregulated capitalism.

    The problem with this version of history? There’s not a shred of evidence to support it.Here anthropologist David Graeber presents a stunning reversal of conventional wisdom. He shows that for more than 5,000 years, since the beginnings of the first agrarian empires, humans have used elaborate credit systems to buy and sell goods—that is, long before the invention of coins or cash. It is in this era, Graeber argues, that we also first encounter a society divided into debtors and creditors. Graeber shows that arguments about debt and debt forgiveness have been at the center of political debates from Italy to China, as well as sparking innumerable insurrections. He also brilliantly demonstrates that the language of the ancient works of law and religion (words like “guilt,” “sin,” and “redemption”) derive in large part from ancient debates about debt, and shape even our most basic ideas of right and wrong. We are still fighting these battles today without knowing it.Debt: The First 5,000 Years is a fascinating chronicle of this little known history—as well as how it has defined human history, and what it means for the credit crisis of the present day and the future of our economy.

    Linear Equations; Vector Spaces; Linear Transformations; Polynomials; Determinants; Elementary canonical Forms; Rational and Jordan Forms; Inner Product Spaces; Operators on Inner Product Spaces; Bilinear Forms For all readers interested in linear algebra.

    But building a machine learning system requires that you make practical decisions: Should you collect more training data? Should you use end-to-end deep learning? How do you deal with your training set not matching your test set? and many more. Historically, the only way to learn how to make these "strategy" decisions has been a multi-year apprenticeship in a graduate program or company. This is a book to help you quickly gain this skill, so that you can become better at building AI systems.

    In this, the fourth edition, Jonathan Baron retains the comprehensive attention to the key questions addressed in the previous editions - How should we think? What, if anything, keeps us from thinking that way? How can we improve our thinking and decision making? - and his expanded treatment of topics such as risk, utilitarianism, Baye's theorem, and moral thinking. With the student in mind, the fourth edition emphasizes the development of an understanding of the fundamental concepts in judgment and decision making. This book is essential reading for students and scholars in judgment and decision making and related fields, including psychology, economics, law, medicine, and business.

    In this seventh edition, Robert L. Heilbroner provides a new theme that connects thinkers as diverse as Adam Smith and Karl Marx. The theme is the common focus of their highly varied ideas—namely, the search to understand how a capitalist society works. It is a focus never more needed than in this age of confusing economic headlines.In a bold new concluding chapter entitled “The End of the Worldly Philosophy?” Heilbroner reminds us that the word “end” refers to both the purpose and limits of economics. This chapter conveys a concern that today’s increasingly “scientific” economics may overlook fundamental social and political issues that are central to economics. Thus, unlike its predecessors, this new edition provides not just an indispensable illumination of our past but a call to action for our future. (amazon.com)

    This work includes differential forms and the elegant forms of Maxwell's equations, and a chapter on probability and statistics. It also illustrates and proves mathematical relations.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.